Fixed Query Startegy Parameter

complexity.py

@@ -47,7 +47,7 @@ def plot_ds(grid_size, loc, X, y, xx, yy, title, seeds=None, colspan=1, rowspan=
     Perform Active Learning
     QueryStrategy (Random Sampling or Uncertainty Sampling)
 '''
-def active(classifiers, datasets, experiments, qs, quota=25, plot_every_n=5):
+def active(classifiers, datasets, experiments, query_strat, quota=25, plot_every_n=5):
     for dataset_index, ((X_src, y_src), (X_tgt, y_tgt)) in enumerate(datasets):
         u_tgt = [None] * len(X_tgt)
         est_src = ce.ComplexityEstimator(X_src, y_src, n_windows=10, nK=1)
@@ -99,8 +99,7 @@ def active(classifiers, datasets, experiments, qs, quota=25, plot_every_n=5):
 
             for i in range(quota):  # Loop through the number of queries
 
-                if qs == 1 :
-                    qs_name = 'RandomSampling'
+                if query_strat == 'RandomSampling' :
                     loc, y_loc = oracle.random_query()  # Sample target using RandomSampling strategy
                     u_tgt[loc] = y_loc
                     X_known.append(X_tgt[loc])
@@ -122,8 +121,7 @@ def active(classifiers, datasets, experiments, qs, quota=25, plot_every_n=5):
                         ax.set_xlabel('Accuracy='+('%.2f' % score).lstrip('0'))
                         w += 1
 
-                elif qs == 2:
-                    qs_name = 'UncertaintySampling'
+                elif query_strat == 'UncertaintySampling':
                     model.fit(X_known, y_known) # Fit model on source only to predict probabilities
                     loc, X_chosen = oracle.uncertainty_sampling(model) # Sample target using UncertaintySampling strategy
                     X_known.append(X_tgt[loc])
@@ -145,9 +143,9 @@ def active(classifiers, datasets, experiments, qs, quota=25, plot_every_n=5):
                         ax.set_xlabel('Accuracy='+('%.2f' % score).lstrip('0'))
                         w += 1
 
-        figure.suptitle(experiments[dataset_index] + qs_name )
+        figure.suptitle(experiments[dataset_index] + query_strat )
         figure.tight_layout()
-        fname = './vis/' + str(experiments[dataset_index] + qs_name) + '.png'
+        fname = './vis/' + str(experiments[dataset_index] + query_strat ) + '.png'
         figure.savefig(fname)
 
     plt.tight_layout()
@@ -157,6 +155,7 @@ def main():
     clfs = [SVC(), GaussianNB(), DecisionTreeClassifier(), MLPClassifier(hidden_layer_sizes=(10,10,10,10,10,10), solver='lbfgs', alpha=2, random_state=1, activation='relu')]
     datasets = []
     experiments = []
+    query_strat = 'RandomSampling'
 
     # datasets.append((make_gaussian_quantiles(n_samples=500, n_features=10, n_classes=2), 
     #                  make_gaussian_quantiles(n_samples=500, n_features=10, n_classes=2)))
@@ -173,7 +172,7 @@ def main():
     datasets.append((mnist.load_mnist(), mnist.load_mnist_rotated()))
     experiments.append('MNIST_vs_MNIST_Rotated')
 
-    active(classifiers=clfs, datasets=datasets, experiments=experiments, qs=1)
+    active(classifiers=clfs, datasets=datasets, experiments=experiments, query_strat=query_strat)
 
 if __name__ == "__main__":
     main()